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RSAFormer: A method of polyp segmentation with region self-attention transformer Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Xuehui Yin, Jun Zeng, Tianxiao Hou, Chao Tang, Chenquan Gan, Deepak Kumar Jain, Salvador García
Colonoscopy has attached great importance to early screening and clinical diagnosis of colon cancer. It remains a challenging task to achieve fine segmentation of polyps. However, existing State-of-the-art models still have limited segmentation ability due to the lack of clear and highly similar boundaries between normal tissue and polyps. To deal with this problem, we propose a region self-attention
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Blood flow and emboli transport patterns during venoarterial extracorporeal membrane oxygenation: A computational fluid dynamics study Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Mehrdad Khamooshi, Avishka Wickramarachchi, Tim Byrne, Michael Seman, David F. Fletcher, Aidan Burrell, Shaun D. Gregory
Despite advances in Venoarterial Extracorporeal Membrane Oxygenation (VA-ECMO), a significant mortality rate persists due to complications. The non-physiological blood flow dynamics of VA-ECMO may lead to neurological complications and organ ischemia. Continuous retrograde high-flow oxygenated blood enters through a return cannula placed in the femoral artery which opposes the pulsatile deoxygenated
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How CLSPN could demystify its prognostic value and potential molecular mechanism for hepatocellular carcinoma: A crosstalk study Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Yanlong Shi, Yizhu Wang, Kaiyi Niu, Wenning Zhang, Qingpeng Lv, Yewei Zhang
CLSPN, a critical component of the S-phase checkpoint in response to DNA replication stress, has been implicated in the pathogenesis of multiple tumor types. The rising incidence of hepatocellular carcinoma (HCC) poses a significant challenge to global public health. Despite this, the specific functions of CLSPN in the development of HCC remain poorly understood. We systematically evaluated the expression
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New candidate genes potentially involved in Zika virus teratogenesis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Miriãn Ferrão Maciel-Fiuza, Bruna Duarte Rengel, Gabriela Elis Wachholz, Julia do Amaral Gomes, Maikel Rosa de Oliveira, Thayne Woycinck Kowalski, Paulo Michel Roehe, Fernanda Sales Luiz Vianna, Lavínia Schüler-Faccini, Fabiana Quoos Mayer, Ana Paula Muterle Varela, Lucas Rosa Fraga
Despite efforts to elucidate Zika virus (ZIKV) teratogenesis, still several issues remain unresolved, particularly on the molecular mechanisms behind the pathogenesis of Congenital Zika Syndrome (CZS). To answer this question, we used bioinformatics tools, animal experiments and human gene expression analysis to investigate genes related to brain development potentially involved in CZS. Searches in
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Impartial feature selection using multi-agent reinforcement learning for adverse glycemic event prediction Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Seo-Hee Kim, Dae-Yeon Kim, Sung-Wan Chun, Jaeyun Kim, Jiyoung Woo
We developed an attention model to predict future adverse glycemic events 30 min in advance based on the observation of past glycemic values over a 35 min period. The proposed model effectively encodes insulin administration and meal intake time using Time2Vec (T2V) for glucose prediction. The proposed impartial feature selection algorithm is designed to distribute rewards proportionally according
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ncRS: A resource of non-coding RNAs in sepsis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Baocai Zhong, Yongfang Dai, Li Chen, Xinying Xu, Yuxi Lan, Leyao Deng, Liping Ren, Nanchao Luo, Lin Ning
Sepsis, a life-threatening condition triggered by the body's response to infection, presents a significant global healthcare challenge characterized by disarrayed host responses, widespread inflammation, organ impairment, and heightened mortality rates. This study introduces the ncRS database (), a meticulously curated repository housing 1144 experimentally validated non-coding RNAs (ncRNAs) intricately
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Exploring the anti-gout potential of sunflower receptacles alkaloids: A computational and pharmacological analysis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Kaiyu Wang, Huizi Cui, Kaifeng Liu, Qizheng He, Xueqi Fu, Wannan Li, Weiwei Han
Gout, a painful condition marked by elevated uric acid levels often linked to the diet's high purine and alcohol content, finds a potential treatment target in xanthine oxidase (XO), a crucial enzyme for uric acid production. This study explores the therapeutic properties of alkaloids extracted from sunflower (Helianthus annuus L.) receptacles against gout. By leveraging computational chemistry and
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Machine learning-based predictive model for abdominal diseases using physical examination datasets Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Wei Chen, YuJie Zhang, Weili Wu, Hui Yang, Wenxiu Huang
Abdominal ultrasound is a key non-invasive imaging method for diagnosing liver, kidney, and gallbladder diseases, despite its clinical significance, not all individuals can undergo abdominal ultrasonography during routine health check-ups due to limitations in equipment, cost, and time. This study aims to use basic physical examination data to predict the risk of diseases of the liver, kidney, and
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An improved RIME optimization algorithm for lung cancer image segmentation Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-11 Lei Guo, Lei Liu, Zhiguang Zhao, Xiaodong Xia
Lung cancer is a prevalent form of cancer worldwide, necessitating early and accurate diagnosis for successful treatment. Within medical imaging processing, image segmentation plays a vital role in medical diagnosis. This study applies swarm intelligence algorithms to segment lung cancer pathological images at three levels. The original algorithm incorporates the Whales' search prey mechanism and a
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A review on machine learning approaches for microalgae cultivation systems Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-10 Tehreem Syed, Felix Krujatz, Yob Ihadjadene, Gunnar Mühlstädt, Homa Hamedi, Jonathan Mädler, Leon Urbas
Microalgae plays a crucial role in biomass production within aquatic environments and are increasingly recognized for their potential in generating biofuels, biomaterials, bioactive compounds, and bio-based chemicals. This growing significance is driven by the need to address imminent global challenges such as food and fuel shortages. Enhancing the value chain of bio-based products necessitates the
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Corrigendum to “Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation” [Comput. Biol. Med. 170 (2024) CIBM-D-23-08582R4] Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-08 Shweta Gangrade, Prakash Chandra Sharma, Akhilesh Kumar Sharma, Yadvendra Pratap Singh
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An effective colorectal polyp classification for histopathological images based on supervised contrastive learning Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-08 Sena Busra Yengec-Tasdemir, Zafer Aydin, Ebru Akay, Serkan Dogan, Bulent Yilmaz
Early detection of colon adenomatous polyps is pivotal in reducing colon cancer risk. In this context, accurately distinguishing between adenomatous polyp subtypes, especially tubular and tubulovillous, from hyperplastic variants is crucial. This study introduces a cutting-edge computer-aided diagnosis system optimized for this task. Our system employs advanced Supervised Contrastive learning to ensure
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Multi-residual 2D network integrating spatial correlation for whole heart segmentation Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Yan Huang, Jinzhu Yang, Qi Sun, Yuliang Yuan, Honghe Li, Yang Hou
Whole heart segmentation (WHS) has significant clinical value for cardiac anatomy, modeling, and analysis of cardiac function. This study aims to address the WHS accuracy on cardiac CT images, as well as the fast inference speed and low graphics processing unit (GPU) memory consumption required by practical clinical applications. Thus, we propose a multi-residual two-dimensional (2D) network integrating
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Multi-scale and multi-view network for lung tumor segmentation Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Caiqi Liu, Han Liu, Xuehui Zhang, Jierui Guo, Pengju Lv
Lung tumor segmentation in medical imaging is a critical step in the diagnosis and treatment planning for lung cancer. Accurate segmentation, however, is challenging due to the variability in tumor size, shape, and contrast against surrounding tissues. In this work, we present MSMV-Net, a novel deep learning architecture that integrates multi-scale multi-view (MSMV) learning modules and multi-scale
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Machine learning on cardiotocography data to classify fetal outcomes: A scoping review Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Farah Francis, Saturnino Luz, Honghan Wu, Sarah J. Stock, Rosemary Townsend
Uterine contractions during labour constrict maternal blood flow and oxygen delivery to the developing baby, causing transient hypoxia. While most babies are physiologically adapted to withstand such intrapartum hypoxia, those exposed to severe hypoxia or with poor physiological reserves may experience neurological injury or death during labour. Cardiotocography (CTG) monitoring was developed to identify
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Identification of hub genes in calcific aortic valve disease Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Qian-Cheng Lai, Jie Zheng, Jian Mou, Chun-Yan Cui, Qing-Chen Wu, Syed M Musa Rizvi, Ying Zhang, Tian -Mei Li, Ying-Bo Ren, Qing Liu, Qun Li, Cheng Zhang
Calcific aortic valve disease (CAVD) is a heart valve disorder characterized primarily by calcification of the aortic valve, resulting in stiffness and dysfunction of the valve. CAVD is prevalent among aging populations and is linked to factors such as hypertension, dyslipidemia, tobacco use, and genetic predisposition, and can result in becoming a growing economic and health burden. Once aortic valve
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Anticipating interpersonal sensitivity: A predictive model for early intervention in psychological disorders in college students Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Min Zhang, Kailei Yan, Yufeng Chen, Ruying Yu
Psychological disorders, notably social anxiety and depression, exert detrimental effects on university students, impeding academic achievement and overall development. Timely identification of interpersonal sensitivity becomes imperative to implement targeted support and interventions. This study selected 958 freshmen from higher education institutions in Zhejiang province as the research sample.
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CSAP-UNet: Convolution and self-attention paralleling network for medical image segmentation with edge enhancement Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Xiaodong Fan, Jing Zhou, Xiaoli Jiang, Meizhuo Xin, Limin Hou
Convolution operation is performed within a local window of the input image. Therefore, convolutional neural network (CNN) is skilled in obtaining local information. Meanwhile, the self-attention (SA) mechanism extracts features by calculating the correlation between tokens from all positions in the image, which has advantage in obtaining global information. Therefore, the two modules can complement
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A thermoregulation model based on the physical and physiological characteristics of Chinese elderly Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Shan Zhou, Linyuan Ouyang, Baizhan Li, Simon Hodder, Runming Yao
Given the increasing aging population and rising living standards in China, developing an accurate and straightforward thermoregulation model for the elderly has become increasingly essential. To address this need, an existing one-segment four-node thermoregulation model for the young was selected as the base model. This study developed the base model considering age-related physical and physiological
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The path from task-specific to general purpose artificial intelligence for medical diagnostics: A bibliometric analysis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Chuheng Chang, Wen Shi, Youyang Wang, Zhan Zhang, Xiaoming Huang, Yang Jiao
Artificial intelligence (AI) has revolutionized many fields, and its potential in healthcare has been increasingly recognized. Based on diverse data sources such as imaging, laboratory tests, medical records, and electrophysiological data, diagnostic AI has witnessed rapid development in recent years. A comprehensive understanding of the development status, contributing factors, and their relationships
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PKSEA-Net: A prior knowledge supervised edge-aware multi-task network for retinal arteriolar morphometry Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Chongjun Huang, Zhuoran Wang, Guohui Yuan, Zhiming Xiong, Jing Hu, Yuhua Tong
Retinal fundus images serve as a non-invasive modality to obtain information pertaining to retinal vessels through fundus photography, thereby offering insights into cardiovascular and cerebrovascular diseases. Retinal arteriolar morphometry has emerged as the most convenient and fundamental clinical methodology in the realm of patient screening and diagnosis. Nevertheless, the analysis of retinal
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Three-dimensional morphology scoring of hepatocellular carcinoma stratifies prognosis and immune infiltration Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Xinxin Wang, Can Yu, Yu Sun, Yixin Liu, Shuli Tang, Yige Sun, Yang Zhou
The morphological attributes could serve as pivotal indicators precipitating early recurrence and dismal overall survival in hepatocellular carcinoma (HCC), and quantifying morphological features may better stratify the prognosis of HCC. To develop a radiomics approach based on 3D tumor morphology features for predicting the prognosis of HCC and identifying differentially expressed genes related to
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Postmenopausal endometrial non-benign lesion risk classification through a clinical parameter-based machine learning model Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-07 Jin Lai, Bo Rao, Zhao Tian, Qing-jie Zhai, Yi-ling Wang, Si-kai Chen, Xin-ting Huang, Hong-lan Zhu, Heng Cui
This study aimed to develop and evaluate a machine learning model utilizing non-invasive clinical parameters for the classification of endometrial non-benign lesions, specifically atypical hyperplasia (AH) and endometrioid carcinoma (EC), in postmenopausal women. Our study collected clinical parameters from a cohort of 999 patients with postmenopausal endometrial lesions and conducted preprocessing
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A novel approach for intelligent diagnosis and grading of diabetic retinopathy Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-06 Zeru Hai, Beiji Zou, Xiaoxia Xiao, Qinghua Peng, Junfeng Yan, Wensheng Zhang, Kejuan Yue
Diabetic retinopathy (DR) is a severe ocular complication of diabetes that can lead to vision damage and even blindness. Currently, traditional deep convolutional neural networks (CNNs) used for DR grading tasks face two primary challenges: (1) insensitivity to minority classes due to imbalanced data distribution, and (2) neglecting the relationship between the left and right eyes by utilizing the
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Bi-level gene selection of cancer by combining clustering and sparse learning Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-06 Junnan Chen, Bo Wen
The diagnosis of cancer based on gene expression profile data has attracted extensive attention in the field of biomedical science. This type of data usually has the characteristics of high dimensionality and noise. In this paper, a hybrid gene selection method based on clustering and sparse learning is proposed to choose the key genes with high precision. We first propose a filter method, which combines
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Computer-assisted discovery and evaluation of potential ribosomal protein S6 kinase beta 2 inhibitors Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-06 Fangyi Yu, Xiaochuan Wu, WeiSong Chen, Fugui Yan, Wen Li
S6K2 is an important protein in mTOR signaling pathway and cancer. To identify potential S6K2 inhibitors for mTOR pathway treatment, a virtual screening of 1,575,957 active molecules was performed using PLANET, AutoDock GPU, and AutoDock Vina, with their classification abilities compared. The MM/PB(GB)SA method was used to identify four compounds with the strongest binding energies. These compounds
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A bagging approach for improved predictive accuracy of intradialytic hypotension during hemodialysis treatment Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-05 Chien-Liang Liu, Min-Hsuan Lee, Shan-Ni Hsueh, Chia-Chen Chung, Chun-Ju Lin, Po-Han Chang, An-Chun Luo, Hsuan-Chi Weng, Yu-Hsien Lee, Ming-Ji Dai, Min-Juei Tsai
The primary objective of this study is to enhance the prediction accuracy of intradialytic hypotension in patients undergoing hemodialysis. A significant challenge in this context arises from the nature of the data derived from the monitoring devices and exhibits an extreme class imbalance problem. Traditional predictive models often display a bias towards the majority class, compromising the accuracy
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Prediction of protein-ATP binding residues using multi-view feature learning via contextual-based co-attention network Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-04 Jia-Shun Wu, Yan Liu, Fang Ge, Dong-Jun Yu
Accurately predicting protein-ATP binding residues is critical for protein function annotation and drug discovery. Computational methods dedicated to the prediction of binding residues based on protein sequence information have exhibited notable advancements in predictive accuracy. Nevertheless, these methods continue to grapple with several formidable challenges, including limited means of extracting
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A bidirectional interpretable compound-protein interaction prediction framework based on cross attention Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-02 Meng Wang, Jianmin Wang, Zhiwei Rong, Liuying Wang, Zhenyi Xu, Liuchao Zhang, Jia He, Shuang Li, Lei Cao, Yan Hou, Kang Li
The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional
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Molecular insights into gastric cancer: The impact of TGFBR2 and hsa-mir-107 revealed by microarray sequencing and bioinformatics Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-02 Zhengsen Jin, Zhihong Huang, Chao Wu, Fanqin Zhang, Yifei Gao, Siyu Guo, Xiaoyu Tao, Shan Lu, Jingyuan Zhang, Jiaqi Huang, Yiyan Zhai, Rui Shi, Peizhi Ye, Jiarui Wu
Gastric carcinoma (GC) remains a significant therapeutic challenge, garnering widespread attention. Oxymatrine (OMT), an active component of the traditional Chinese medicine compound Kushen injection (CKI), has shown promising results in combination with chemotherapy for the treatment of GC. However, the molecular mechanisms underlying OMT's therapeutic effects in GC have yet to be elucidated. The
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Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-02 Nuno Miguel Rodrigues, José Guilherme de Almeida, Ana Sofia Castro Verde, Ana Mascarenhas Gaivão, Carlos Bilreiro, Inês Santiago, Joana Ip, Sara Belião, Raquel Moreno, Celso Matos, Leonardo Vanneschi, Manolis Tsiknakis, Kostas Marias, Daniele Regge, Sara Silva, Nickolas Papanikolaou
Despite being one of the most prevalent forms of cancer, prostate cancer (PCa) shows a significantly high survival rate, provided there is timely detection and treatment. Computational methods can help make this detection process considerably faster and more robust. However, some modern machine-learning approaches require accurate segmentation of the prostate gland and the index lesion. Since performing
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Critical roles of S100A12, MMP9, and PRTN3 in sepsis diagnosis: Insights from multiple microarray data analyses Comput. Biol. Med. (IF 7.7) Pub Date : 2024-03-01 Wenyuan Zhang
Sepsis, characterized by systemic inflammatory response syndrome and life-threatening organ dysfunction, remains a significant global cause of disability and death. Despite its impact, reliable biomarkers for sepsis diagnosis are yet to be identified. This study aims to investigate and identify key genes and pathways in sepsis through the analysis of multiple microarray datasets, providing potential
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Automatic bright-field smear microscopy for diagnosis of pulmonary tuberculosis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-29 Mikaela Kalline Maciel Serrão, Marly Guimarães Fernandes Costa, Luciana Botinelly Mendonça Fujimoto, Mauricio Morishi Ogusku, Cicero Ferreira Fernandes Costa Filho
In recent decades, many studies have been published on the use of automatic smear microscopy for diagnosing pulmonary tuberculosis (TB). Most of them deal with a preliminary step of the diagnosis, the bacilli detection, whereas sputum smear microscopy for diagnosis of pulmonary TB comprises detecting and reporting the number of bacilli found in at least 100 microscopic fields, according to the 5 grading
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Intra-sample reversed pairs based on differentially ranked genes reveal biosignature for ovarian cancer Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-29 Pengfei Zhao, Dian Meng, Zunkai Hu, Yining Liang, Yating Feng, Tongjie Sun, Lixin Cheng, Xubin Zheng, Haili Li
Ovarian cancer, a major gynecological malignancy, often remains undetected until advanced stages, necessitating more effective early screening methods. Existing biomarker based on differential genes often suffers from variations in clinical practice. To overcome the limitations of absolute gene expression values including batch effects and biological heterogeneity, we introduced a pairwise biosignature
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scAuto as a comprehensive framework for single-cell chromatin accessibility data analysis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-29 Meiqin Gong, Yun Yu, Zixuan Wang, Junming Zhang, Xiongyi Wang, Cheng Fu, Yongqing Zhang, Xiaodong Wang
Interpreting single-cell chromatin accessibility data is crucial for understanding intercellular heterogeneity regulation. Despite the progress in computational methods for analyzing this data, there is still a lack of a comprehensive analytical framework and a user-friendly online analysis tool. To fill this gap, we developed a pre-trained deep learning-based framework, single-cell auto-correlation
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CODENET: A deep learning model for COVID-19 detection Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-29 Hong Ju, Yanyan Cui, Qiaosen Su, Liran Juan, Balachandran Manavalan
Conventional COVID-19 testing methods have some flaws: they are expensive and time-consuming. Chest X-ray (CXR) diagnostic approaches can alleviate these flaws to some extent. However, there is no accurate and practical automatic diagnostic framework with good interpretability. The application of artificial intelligence (AI) technology to medical radiography can help to accurately detect the disease
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Uncovering driver genes in breast cancer through an innovative machine learning mutational analysis method Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-29 Golnaz Taheri, Mahnaz Habibi
Breast cancer has become a severe public health concern and one of the leading causes of cancer-related death in women worldwide. Several genes and mutations in these genes linked to breast cancer have been identified using sophisticated techniques, despite the fact that the exact cause of breast cancer is still unknown. A commonly used feature for identifying driver mutations is the recurrence of
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Coarse-to-fine visual representation learning for medical images via class activation maps Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-29 Boon Peng Yap, Beng Koon Ng
The value of coarsely labeled datasets in learning transferable representations for medical images is investigated in this work. Compared to fine labels which require meticulous effort to annotate, coarse labels can be acquired at a significantly lower cost and can provide useful training signals for data-hungry deep neural networks. We consider coarse labels in the form of binary labels differentiating
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PECI-Net: Bolus segmentation from video fluoroscopic swallowing study images using preprocessing ensemble and cascaded inference Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-29 Dougho Park, Younghun Kim, Harim Kang, Junmyeoung Lee, Jinyoung Choi, Taeyeon Kim, Sangeok Lee, Seokil Son, Minsol Kim, Injung Kim
Bolus segmentation is crucial for the automated detection of swallowing disorders in videofluoroscopic swallowing studies (VFSS). However, it is difficult for the model to accurately segment a bolus region in a VFSS image because VFSS images are translucent, have low contrast and unclear region boundaries, and lack color information. To overcome these challenges, we propose PECI-Net, a network architecture
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ECG-based data-driven solutions for diagnosis and prognosis of cardiovascular diseases: A systematic review Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Pedro A. Moreno-Sánchez, Guadalupe García-Isla, Valentina D.A. Corino, Antti Vehkaoja, Kirsten Brukamp, Mark van Gils, Luca Mainardi
Cardiovascular diseases (CVD) are a leading cause of death globally, and result in significant morbidity and reduced quality of life. The electrocardiogram (ECG) plays a crucial role in CVD diagnosis, prognosis, and prevention; however, different challenges still remain, such as an increasing unmet demand for skilled cardiologists capable of accurately interpreting ECG. This leads to higher workload
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Unexpected effect of halogenation on the water solubility of small organic compounds Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Jianfang Zhang, Kinga Virág Gulyás, Jintian Li, Minfei Ma, Liping Zhou, Leyun Wu, Ruisheng Xiong, Mate Erdelyi, Weiliang Zhu, Zhijian Xu
Halogenation is an indispensable method in the structural modification of lead compounds. It is known to increase lipophilicity and is hence used to improve membrane permeability and thus bioavailability. In this study, we compare the water solubility (logS) of organohalogen compounds and their non-halogenated parent compounds using the molecular matched pair (MMP) analysis method. Unexpectedly, 19
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Unsupervised domain adaptation for histopathology image segmentation with incomplete labels Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Huihui Zhou, Yan Wang, Benyan Zhang, Chunhua Zhou, Maxim S. Vonsky, Lubov B. Mitrofanova, Duowu Zou, Qingli Li
Stain variations pose a major challenge to deep learning segmentation algorithms in histopathology images. Current unsupervised domain adaptation methods show promise in improving model generalization across diverse staining appearances but demand abundant accurately labeled source domain data. This paper assumes a novel scenario, namely, unsupervised domain adaptation based segmentation task with
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Automatic identification of hypertension and assessment of its secondary effects using artificial intelligence: A systematic review (2013–2023) Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Anjan Gudigar, Nahrizul Adib Kadri, U. Raghavendra, Jyothi Samanth, M. Maithri, Mahesh Anil Inamdar, Mukund A. Prabhu, Ajay Hegde, Massimo Salvi, Chai Hong Yeong, Prabal Datta Barua, Filippo Molinari, U. Rajendra Acharya
Artificial intelligence (AI) techniques are increasingly used in computer-aided diagnostic tools in medicine. These techniques can also help to identify hypertension (HTN) in its early stage, as it is a global health issue. Automated HTN detection uses socio-demographic, clinical data, and physiological signals. Additionally, signs of secondary HTN can also be identified using various imaging modalities
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ECPC-IDS: A benchmark endometrial cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Dechao Tang, Chen Li, Tianmin Du, Huiyan Jiang, Deguo Ma, Zhiyu Ma, Marcin Grzegorzek, Tao Jiang, Hongzan Sun
Endometrial cancer is one of the most common tumors in the female reproductive system and is the third most common gynecological malignancy that causes death after ovarian and cervical cancer. Early diagnosis can significantly improve the 5-year survival rate of patients. With the development of artificial intelligence, computer-assisted diagnosis plays an increasingly important role in improving the
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CFViSA: A comprehensive and free platform for visualization and statistics in omics-data Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Ningqi Wang, Yarong Li, Shuwen Han, Yaozhong Zhang, Jun Yang, Zheng Yin, Cong Deng, Zijing Liu, Yinhang Wu, Wei Wu, Wei Xue, Tianjie Yang, Yangchun Xu, Qirong Shen, Gaofei Jiang, Zhong Wei
The rapid growth of omics technologies has led to the use of bioinformatics as a powerful tool for unravelling scientific puzzles. However, the obstacles of bioinformatics are compounded by the complexity of data processing and the distinct nature of omics data types, particularly in terms of visualization and statistics. We developed a comprehensive and free platform, CFViSA, to facilitate effortless
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Enhancing the accuracy of shock advisory algorithms in automated external defibrillators during ongoing cardiopulmonary resuscitation using a cascade of CNNEDs Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Mahdi Pirayesh Shirazi Nejad, Vadym Kargin, Shirin Hajeb-M, David Hicks, Matt Valentine, K.H. Chon
Delivery of continuous cardiopulmonary resuscitation (CPR) plays an important role in the out-of-hospital cardiac arrest (OHCA) survival rate. However, to prevent CPR artifacts being superimposed on ECG morphology data, currently available automated external defibrillators (AEDs) require pauses in CPR for accurate analysis heart rhythms. In this study, we propose a novel Convolutional Neural Network-based
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Using LDDMM and a kinematic cardiac growth model to quantify growth and remodelling in rat hearts under PAH Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Debao Guan, Lian Tian, Wei Li, Hao Gao
Pulmonary arterial hypertension (PAH) is a rapidly progressive and fatal disease, with right ventricular failure being the primary cause of death in patients with PAH. This study aims to determine the mechanical stimuli that may initiate heart growth and remodelling (G&R). To achieve this, two bi-ventricular models were constructed: one for a control rat heart and another for a rat heart with PAH.
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Transcriptomic signature of cancer cachexia by integration of machine learning, literature mining and meta-analysis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Kening Zhao, Esmaeil Ebrahimie, Manijeh Mohammadi Dehcheshmeh, Mathew G. Lewsey, Lei Zheng, Nick J. Hoogenraad
Cancer cachexia is a severe metabolic syndrome marked by skeletal muscle atrophy. A successful clinical intervention for cancer cachexia is currently lacking. The study of cachexia mechanisms is largely based on preclinical animal models and the availability of high-throughput transcriptomic datasets of cachectic mouse muscles is increasing through the extensive use of next generation sequencing technologies
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Neighborhood evaluator for efficient super-resolution reconstruction of 2D medical images Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Zijia Liu, Jing Han, Jiannan Liu, Zhi-Cheng Li, Guangtao Zhai
Deep learning-based super-resolution (SR) algorithms aim to reconstruct low-resolution (LR) images into high-fidelity high-resolution (HR) images by learning the low- and high-frequency information. Experts’ diagnostic requirements are fulfilled in medical application scenarios through the high-quality reconstruction of LR digital medical images. Medical image SR algorithms should satisfy the requirements
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Cardiovascular hardware simulator and artificial aorta-generated central blood pressure waveform database according to various vascular ages for cardiovascular health monitoring applications Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Jae-Hak Jeong, Bomi Lee, Junki Hong, Changhee Min, Adelle Ria Persad, Tae-Heon Yang, Yong-Hwa Park
This study presents a database of central blood pressure waveforms according to cardiovascular health conditions, to supplement the lack of clinical data in cardiovascular health research, constructed by a cardiovascular simulator. Blood pressure (BP) is the most frequently measured biomarker, and in addition to systolic and diastolic pressure, its waveform represents the various conditions of cardiovascular
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Weakly supervised learning for multi-class medical image segmentation via feature decomposition Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Zhuo Kuang, Zengqiang Yan, Li Yu
Weakly supervised learning with image-level labels, releasing deep learning from highly labor-intensive pixel-wise annotation, has gained great attention for medical image segmentation. However, existing weakly supervised methods are mainly designed for single-class segmentation while leaving multi-class medical image segmentation rarely-explored. Different from natural images, label symbiosis, together
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The influence of resolution on the predictive power of spatial heterogeneity measures as biomarkers of liver fibrosis Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Jari Claes, Annelies Agten, Alfonso Blázquez-Moreno, Marjolein Crabbe, Marianne Tuefferd, Hinrich Goehlmann, Helena Geys, Cheng-Yuan Peng, Thomas Neyens, Christel Faes
Spatial heterogeneity of cells in liver biopsies can be used as biomarker for disease severity of patients. This heterogeneity can be quantified by non-parametric statistics of point pattern data, which make use of an aggregation of the point locations. The method and scale of aggregation are usually chosen ad hoc, despite values of the aforementioned statistics being heavily dependent on them. Moreover
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Multiscale differential geometry learning of networks with applications to single-cell RNA sequencing data Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Hongsong Feng, Sean Cottrell, Yuta Hozumi, Guo-Wei Wei
Single-cell RNA sequencing (scRNA-seq) has emerged as a transformative technology, offering unparalleled insights into the intricate landscape of cellular diversity and gene expression dynamics. scRNA-seq analysis represents a challenging and cutting-edge frontier within the field of biological research. Differential geometry serves as a powerful mathematical tool in various applications of scientific
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LumVertCancNet: A novel 3D lumbar vertebral body cancellous bone location and segmentation method based on hybrid Swin-transformer Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-28 Yingdi Zhang, Zelin Shi, Huan Wang, Shaoqian Cui, Lei Zhang, Jiachen Liu, Xiuqi Shan, Yunpeng Liu, Lei Fang
Lumbar vertebral body cancellous bone location and segmentation is crucial in an automated lumbar spine processing pipeline. Accurate and reliable analysis of lumbar spine image is expected to advantage practical medical diagnosis and population-based analysis of bone strength. However, the design of automated algorithms for lumbar spine processing is demanding due to significant anatomical variations
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Segment anything model for medical image segmentation: Current applications and future directions Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-27 Yichi Zhang, Zhenrong Shen, Rushi Jiao
Due to the inherent flexibility of prompting, foundation models have emerged as the predominant force in the fields of natural language processing and computer vision. The recent introduction of the Segment Anything Model (SAM) signifies a noteworthy expansion of the prompt-driven paradigm into the domain of image segmentation, thereby introducing a plethora of previously unexplored capabilities. However
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Development of a prognostic model for muscle-invasive bladder cancer using glutamine metabolism Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-27 Sida Hao, Lin Shen, Pengju Liu, Qin Yong, Yeqiang Wang, Xiangyi Zheng
Muscle-invasive bladder cancer (MIBC) is distinguished by its pronounced invasiveness and unfavorable prognosis. Immunotherapy and targeted therapy have emerged as key treatment options for various types of cancer. Altered metabolism is a defining characteristic of cancer cells, and there is mounting evidence suggesting the important role of glutamine metabolism (GM) in tumor metabolism. Nevertheless
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Efficiently improving the Wi-Fi-based human activity recognition, using auditory features, autoencoders, and fine-tuning Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-27 Amir Rahdar, Mahnaz Chahoushi, Seyed Ali Ghorashi
Human activity recognition (HAR) based on Wi-Fi signals has attracted significant attention due to its convenience and the availability of infrastructures and sensors. Channel State Information (CSI) measures how Wi-Fi signals propagate through the environment. However, many scenarios and applications have insufficient training data due to constraints such as cost, time, or resources. This poses a
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Reconstructing microvascular network skeletons from 3D images: What is the ground truth? Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-27 Claire L. Walsh, Maxime Berg, Hannah West, Natalie A. Holroyd, Simon Walker-Samuel, Rebecca J. Shipley
Structural changes to microvascular networks are increasingly highlighted as markers of pathogenesis in a wide range of disease, e.g. Alzheimer’s disease, vascular dementia and tumour growth. This has motivated the development of dedicated 3D imaging techniques, alongside the creation of computational modelling frameworks capable of using 3D reconstructed networks to simulate functional behaviours
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SinCWIm: An imputation method for single-cell RNA sequence dropouts using weighted alternating least squares Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-27 Lejun Gong, Xiong Cui, Yang Liu, Cai Lin, Zhihong Gao
Single-cell RNA sequencing (scRNA-seq) provides a powerful tool for exploring cellular heterogeneity, discovering novel or rare cell types, distinguishing between tissue-specific cellular composition, and understanding cell differentiation during development. However, due to technological limitations, dropout events in scRNA-seq can mistakenly convert some entries in the real data to zero. This is
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Analysis and design of antithetic proportional-integral-derivative biocontrol-systems with species dilution Comput. Biol. Med. (IF 7.7) Pub Date : 2024-02-27 Xun Deng, Hui Lv, Qiang Zhang, Edmund Ming Kit Lai
The nonlinearity and non-separability of the antithetic PID (aPID) controller have provided greater flexibility in the design of biochemical reaction networks (BCRNs), resulting in significant impacts on biocontrol-systems. Nevertheless, the dilution of control species is disregarded in designs of aPID controllers, which would lead to the failure of inhibition mechanism in the controller and loss of